PNNL

Abstract

In this study, a pair of convection-permitting (2-km grid spacing), month-long, wet season Advanced Weather Research and Forecasting (WRF) simulations with and without the Eddy-Diffusivity Mass-Flux (EDMF) scheme are performed for a portion of the Green Ocean Amazon (GoAmazon) 2014/5 field campaign period. EDMF produces an ensemble of subgrid-scale convective plumes that evolve in response to the boundary layer meteorology and can develop into shallow clouds. The objective of this study is to determine how different treatments of shallow cumulus clouds impact the total cloud population and precipitation in the Amazonian rainforest, with emphasis on impacts on the likelihood of shallow-to-deep convection transitions. Results indicate that the large-scale synoptic conditions in the EDMF and control simulations are nearly identical, however, on the local scale, their rainfall patterns diverge drastically, especially overnight. The EDMF scheme significantly increases the frequency of shallow clouds, however, the frequencies of deep clouds are similar between the simulations. Precipitation biases are reduced with the use of EDMF, especially overnight. Deep convective clouds (DCC) are tracked using a cloud tracking algorithm to examine the impact of shallow cumulus on the surrounding ambient environment where deep convective clouds initiate in. Results suggest that a rapid increase of low-level cloudiness acts to cool and moisten the low to-mid troposphere, favoring the transistion to deep convection. Importantly, averages of traditional convective metrics are unable to distinguish between favorable and non-favorable deep convective environments.